Jones, M. C.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1016/0167-7152(92)90107-G|
|Google Scholar:||Look up in Google Scholar|
Recently, much progress has been made on understanding the bandwidth selection problem in kernel density estimation. Here, analogous questions are considered for extensions to the basic problem, namely, for estimating derivatives, using ‘better’ kernel estimators, and for the multivariate case. In basic kernel density estimation, recent advances have resulted in considerable improvements being made over ‘moderate’ methods such as least squares cross-validation. Here, it is argued that, in the first two extension cases, the performance of moderate methods deteriorates even more, so that the necessity for ‘improved’ methods — and indeed their potential in theory if not necessarily in practice — is greatly increased. Rather extraordinary things happen, however, when higher dimensions are considered. This paper is essentially that of Jones (1991).
|Item Type:||Journal Article|
|Copyright Holders:||1992 Elsevier Science B.V.|
|Keywords:||adaptive selection; convergence rates; cross-validation; estimating derivatives; functional estimation; higher order kernels; multivariate estimation; smoothing|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||24 Mar 2011 11:26|
|Last Modified:||04 Oct 2016 11:01|
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